Data profiling provides a powerful tool for uncovering deep insights into the structure and quality of your data through advanced techniques that detect anomalies, deviations, and patterns. By thoroughly analyzing data at both the attribute and record level, profiling helps to identify inconsistencies, gaps, or outliers that could impact data integrity. Continuous monitoring of these metrics ensures that potential issues are flagged early, allowing you to maintain a high standard of data reliability and accuracy. With proactive oversight of data quality, you can address problems before they escalate, ensuring that your data remains trustworthy and fit for critical business use.
Deep insights into data
Data profiling provides deep insights into your data by offering a comprehensive column-level overview of available values and their distribution. With this feature, users can quickly access aggregated metrics such as the count of empty and non-empty values, the number of unique values, and statistical measures like minimum, maximum, and average values. These insights allow you to understand the structure, completeness, and variability of your data, helping to identify any inconsistencies or potential issues.
Track data changes
Data profiling enables you to track changes in your data over time by analyzing how aggregated values for each column evolve. This feature provides valuable insights into trends and shifts in data quality, allowing you to monitor key metrics. By visualizing these changes through graphs, you can easily detect outliers and anomalies that may indicate potential issues or shifts in data patterns.
Anomaly detection
The SelectZero platform automatically analyzes how data values change over time, establishing patterns and using machine-learning algorithms to calculate dynamic thresholds. This advanced approach allows the platform to detect anomalies and deviations from expected data behavior, ensuring that any irregularities are identified early. By continuously monitoring data trends, the system can automatically flag potential issues and alert responsible users as soon as anomalies are detected.
